A brief screening tool for knee pain in primary care (KNEST). 2. Results from a survey in the general population aged 50 and over
Bibliographic record
Abstract
OBJECTIVE: To use a brief screening tool to identify knee pain (all knee pain, non-chronic and chronic knee pain) and associated health-care use in the general population aged 50 yr and over. METHODS: A cross-sectional survey was mailed to 8995 individuals registered with three general practices in North Staffordshire, UK. The questionnaire included a Knee Pain Screening Tool (KNEST), the Short Form 36 (SF36), demographic questions and, for those who reported knee pain, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). RESULTS: The survey achieved a 77% response. The 12-month period prevalence of all knee pain was 46.8% [95% confidence interval (CI) 45.6%, 48.0%]. Figures for non-chronic knee pain (pain of less than 3 months duration) and chronic knee pain (pain of more than 3 months duration) were 21.5% (95% CI 20.5%, 22.5%) and 25.3% (95% CI 24.3%, 26.4%) respectively. An estimated 6% of the older population had non-chronic but severe knee pain or disability. Thirty-three per cent of all knee pain sufferers had consulted their general practitioner (GP) about their symptom in the last year. This included 34% of those with non-chronic but severe knee pain or disability and 56% of those with chronic and severe knee pain or disability. The use of private treatments or services for knee pain was minimal. A third of those with chronic and severe knee pain or disability had not used any services (including GP) in the last year. CONCLUSIONS: The KNEST is a simple tool for the identification of individuals with knee pain and their health-care use. Focusing only on chronic knee pain will underestimate the total need and demand for health-care in knee pain sufferers in the general older population, as non-chronic as well as chronic knee pain has a significant impact on people's lives and on their use of primary health-care. The KNEST, when combined with the WOMAC, identifies population groups who have potentially diverse health-care needs and who might benefit from effective health-care. These data can be used alongside evidence on effective treatments by service planners when considering needs for the care of older adults in primary care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".